The Hallucination Problem Is Structural, Not a Bug
When you ask a general-purpose AI model about a stock's PE ratio, momentum score, or volatility regime — it generates text that sounds like analysis. Sometimes it's right. Sometimes it fabricates a number with the same confidence it would use if it were correct.
This is not a GPT problem. It's not a Gemini problem. It's a category problem.
Language models are trained to predict plausible next tokens. In financial analysis, plausible-sounding and factually-grounded are two completely different things.
Why Generic Finance AI Fails
No deterministic backbone. A model that hasn't had structured market signals computed and injected into its context will hallucinate them. "The stock has strong momentum" is not a statement derived from data — it's a pattern-matched plausible string.
No auditability. When a generic tool says a company has a P/E of 22x and it's actually 34x, there is no trace. No computation log. No engine output to inspect. The model generated it, and it's gone.
No regime awareness. Asset performance means nothing in isolation. A 3% drawdown in a bull regime and the same drawdown in a fragility regime are fundamentally different signals. Generic models have no access to this context.
The Fix: Compute First, Interpret Second
LyraIQ's architecture enforces a strict two-phase pipeline: